陈敏教授学术报告

发布时间:2018年09月20日 作者:   消息来源:    阅读次数:[]

报告题目:Sure Explained Variability and Independence Screening
报告地点:数理楼小报告厅
报告时间:2018年9月22日(星期六)下午2:00-5:00
报告摘要:In the era of Big Data, extracting the most important exploratory variables available in ultrahigh dimensional data plays a key role in scienti_c researches. Existing researches have been mainly focusing on applying the extracted exploratory variables to describe the central tendency of their related response variables. For a response variable, its variability characteristic is as much important as the central tendency in statistical inference. This paper focuses on the variability and proposes a new model-free feature screening approach: sure explained variability and independence screening (SEVIS). The core of SEVIS is to take the advantage of recently proposed asymmetric and nonlinear generalized measures of correlation in the screening. Under some mild conditions, the paper shows that SEVIS not only possesses desired sure screening property and ranking consistency property, but also is a computational convenient variable selection method to deal with ultrahigh-dimensional data sets with more features than observations. The superior performance of SEVIS, compared with existing model-free methods, is illustrated in extensive simulations. A real example in ultrahigh-dimensional variable selection demonstrates that the variables selected by SEVIS better explain not only the response variables, but also the variables selected by other methods.
报告人简介:陈敏,中国科学院数学与系统科学研究院二级研究员,博士生导师。现任中国科学院政府行政管理系统分析研究中心主任,全国统计方法应用技术标准化委员会主任委员,《数理统计与管理》主编,《应用数学学报(中文版)》副主编,《中医药现代化》编委。中国数学学会副理事长、中国统计教育学会副会长、北京大数据协会副会长。曾任中国科学院数学与系统科学研究院任副院长,享受国务院政府特殊津贴。<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;主要研究方向为:金融统计理论与方法、非线性时间序列的统计分析,非参数统计估计和检验的大样本理论,生物统计的理论和方法,应用统计(工业统计、统计标准化、财税信息技术),大数据分析与处理的统计理论与算法研究。出版和翻译教材和专著7部;在国内外核心学术期刊发表统计理论与应用、经济、金融和管理科学论文130余篇,其中SCI和EI论文90余篇。



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